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Provides summary of verifying shocks' normality

Source: R/summary.R
summary.SDDRidMIX.Rd

Provides summary of the Savage-Dickey density ratios for verification of structural shocks normality. The outcomes can be used to make probabilistic statements about identification through non-normality.

Usage

# S3 method for class 'SDDRidMIX'
summary(object, ...)

Arguments

object

an object of class SDDRidMIX obtained using the verify_identification.PosteriorBSVARMIX function.

...

additional arguments affecting the summary produced.

Value

A table reporting the logarithm of Bayes factors of normal to non-normal shocks posterior odds "log(SDDR)" for each structural shock, their numerical standard errors "NSE", and the implied posterior probability of the normality and non-normality hypothesis, "Pr[normal|data]" and "Pr[non-normal|data]" respectively.

See also

verify_identification.PosteriorBSVARMIX

Author

Tomasz Woźniak wozniak.tom@pm.me

Examples

# upload data
data(us_fiscal_lsuw)

# specify the model and set seed
specification  = specify_bsvar_mix$new(us_fiscal_lsuw, M = 2)
#> The identification is set to the default option of lower-triangular structural matrix.
set.seed(123)

# estimate the model
posterior      = estimate(specification, 10)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#>  Gibbs sampler for the SVAR-finiteMIX model             |
#> **************************************************|
#>  Progress of the MCMC simulation for 10 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|

# verify heteroskedasticity
sddr           = verify_identification(posterior)
summary(sddr)
#>  **************************************************|
#>  bsvars: Bayesian Structural Vector Autoregressions|
#>  **************************************************|
#>    Summary of identification verification          |
#>    H0: s^2_nm  = 1 for all m  [normal]             |
#>    H1: s^2_nm != 1 for some m [non-normal]         |
#>  **************************************************|
#>          log(SDDR) NSE Pr[H0|data] Pr[H1|data]
#> shock 1  2.3172727   0   0.9102975  0.08970251
#> shock 2 -0.8853491   0   0.2920705  0.70792946
#> shock 3 -1.3323010   0   0.2087790  0.79122100

# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
  specify_bsvar_mix$new(M = 2) |>
  estimate(S = 10) |> 
  verify_identification() |> 
  summary() -> sddr_summary
#> The identification is set to the default option of lower-triangular structural matrix.
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#>  Gibbs sampler for the SVAR-finiteMIX model             |
#> **************************************************|
#>  Progress of the MCMC simulation for 10 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|
#>  **************************************************|
#>  bsvars: Bayesian Structural Vector Autoregressions|
#>  **************************************************|
#>    Summary of identification verification          |
#>    H0: s^2_nm  = 1 for all m  [normal]             |
#>    H1: s^2_nm != 1 for some m [non-normal]         |
#>  **************************************************|